A C-terminal motif found in the beta2-adrenergic receptor, P2Y1 receptor and cystic fibrosis transmembrane conductance regulator determines binding to the Na+/H+ exchanger regulatory factor family of PDZ proteins.

The Na+/H+ exchanger regulatory factor (NHERF) binds to the tail of the beta2-adrenergic receptor and plays a role in adrenergic regulation of Na+/H+ exchange. NHERF contains two PDZ domains, the first of which is required for its interaction with the beta2 receptor. Mutagenesis studies of the beta2 receptor tail revealed that the optimal C-terminal motif for binding to the first PDZ domain of NHERF is D-S/T-x-L, a motif distinct from those recognized by other PDZ domains. The first PDZ domain of NHERF-2, a protein that is 52% identical to NHERF and also known as E3KARP, SIP-1, and TKA-1, exhibits binding preferences very similar to those of the first PDZ domain of NHERF. The delineation of the preferred binding motif for the first PDZ domain of the NHERF family of proteins allows for predictions for other proteins that may interact with NHERF or NHERF-2. For example, as would be predicted from the beta2 receptor tail mutagenesis studies, NHERF binds to the tail of the purinergic P2Y1 receptor, a seven-transmembrane receptor with an intracellular C-terminal tail ending in D-T-S-L. NHERF also binds to the tail of the cystic fibrosis transmembrane conductance regulator, which ends in D-T-R-L. Because the preferred binding motif of the first PDZ domain of the NHERF family of proteins is found at the C termini of a variety of intracellular proteins, NHERF and NHERF-2 may be multifunctional adaptor proteins involved in many previously unsuspected aspects of intracellular signaling.

beta2-Adrenergic receptor regulation by GIT1, a G protein-coupled receptor kinase-associated ADP ribosylation factor GTPase-activating protein.

G protein-coupled receptor activation leads to the membrane recruitment and activation of G protein-coupled receptor kinases, which phosphorylate receptors and lead to their inactivation. We have identified a novel G protein-coupled receptor kinase-interacting protein, GIT1, that is a GTPase-activating protein (GAP) for the ADP ribosylation factor (ARF) family of small GTP-binding proteins. Overexpression of GIT1 leads to reduced beta2-adrenergic receptor signaling and increased receptor phosphorylation, which result from reduced receptor internalization and resensitization. These cellular effects of GIT1 require its intact ARF GAP activity and do not reflect regulation of GRK kinase activity. These results suggest an essential role for ARF proteins in regulating beta2-adrenergic receptor endocytosis. Moreover, they provide a mechanism for integration of receptor activation and endocytosis through regulation of ARF protein activation by GRK-mediated recruitment of the GIT1 ARF GAP to the plasma membrane.

cDNA for the human beta 2-adrenergic receptor: a protein with multiple membrane-spanning domains and encoded by a gene whose chromosomal location is shared with that of the receptor for platelet-derived growth factor.

We have isolated and sequenced a cDNA encoding the human beta 2-adrenergic receptor. The deduced amino acid sequence (413 residues) is that of a protein containing seven clusters of hydrophobic amino acids suggestive of membrane-spanning domains. While the protein is 87% identical overall with the previously cloned hamster beta 2-adrenergic receptor, the most highly conserved regions are the putative transmembrane helices (95% identical) and cytoplasmic loops (93% identical), suggesting that these regions of the molecule harbor important functional domains. Several of the transmembrane helices also share lesser degrees of identity with comparable regions of select members of the opsin family of visual pigments. We have localized the gene for the beta 2-adrenergic receptor to q31-q32 on chromosome 5. This is the same position recently determined for the gene encoding the receptor for platelet-derived growth factor and is adjacent to that for the FMS protooncogene, which encodes the receptor for the macrophage colony-stimulating factor.

Control of cyclin B1 localization through regulated binding of the nuclear export factor CRM1.

Activation of the Cyclin B/Cdc2 kinase complex triggers entry into mitosis in all eukaryotic cells. Cyclin B1 localization changes dramatically during the cell cycle, precipitously transiting from the cytoplasm to the nucleus at the beginning of mitosis. Presumably, this relocalization promotes the phosphorylation of nuclear targets critical for chromatin condensation and nuclear envelope breakdown. We show here that the previously characterized cytoplasmic retention sequence of Cyclin B1, responsible for its interphase cytoplasmic localization, is actually an autonomous nuclear export sequence, capable of directing nuclear export of a heterologous protein, and able to bind specifically to the recently identified export mediator, CRM1. We propose that the observed cytoplasmic localization of Cyclin B1 during interphase reflects the equilibrium between ongoing nuclear import and rapid CRM1-mediated export. In support of this hypothesis, we found that treatment of cells with leptomycin B, which disrupted Cyclin B1-CRM1 interactions, led to a marked nuclear accumulation of Cyclin B1. In mitosis, Cyclin B1 undergoes phosphorylation at several sites, a subset of which have been proposed to play a role in Cyclin B1 accumulation in the nucleus. Both CRM1 binding and the ability to direct nuclear export were affected by mutation of these phosphorylation sites; thus, we propose that Cyclin B1 phosphorylation at the G2/M transition prevents its interaction with CRM1, thereby reducing nuclear export and facilitating nuclear accumulation.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.